AI/ML Engineer - Predictive Modeling

About the Role :

We are looking for an experienced AI/ML Engineer to design, develop, deploy, and optimize machine learning and AI solutions that solve real-world business problems.

The ideal candidate will have strong hands-on experience across the full ML lifecyclefrom data preparation and model development to deployment, monitoring, and continuous improvement in production environments.

This role requires close collaboration with data scientists, software engineers, product managers, and business stakeholders to translate complex requirements into scalable and high-performing AI/ML systems.

Key Responsibilities :

- Design, develop, and deploy machine learning models for production use cases across domains such as predictive analytics, NLP, computer vision, or generative AI Build end-to-end ML pipelines including data ingestion, feature engineering, model training, validation, deployment, and monitoring

- Work with structured and unstructured data at scale, ensuring data quality, reliability, and performance

- Optimize model performance, scalability, latency, and cost in production environments Implement MLOps best practices including CI/CD for ML, versioning, monitoring, retraining, and model governance

- Collaborate with cross-functional teams to understand business problems and convert them into AI-driven solutions

- Deploy and manage models using cloud platforms and containerized environments Conduct model evaluation, bias analysis, and explainability to ensure responsible AI practices

- Document solutions, workflows, and technical decisions for knowledge sharing and maintainability Stay updated with the latest AI/ML research, tools, and industry trends

Required Skills & Experience :

Technical Skills :

- 5+ years of hands-on experience in Machine Learning / AI engineering

- Strong proficiency in Python and experience with ML/DL libraries such as TensorFlow, PyTorch, Scikit-learn, NumPy, Pandas

- Solid understanding of ML algorithms, statistics, and data science fundamentals

- Experience with NLP, Computer Vision, or Generative AI / LLMs (GPT, BERT, T5, LLaMA, etc.)

- Hands-on experience with model deployment using REST APIs, microservices, or batch inference

- Experience with MLOps tools and practices (MLflow, Airflow, Kubeflow, SageMaker, Vertex AI, Azure ML, etc.)

- Strong knowledge of SQL and experience working with large datasets

- Experience with Docker, Kubernetes, and CI/CD pipelines Cloud experience on AWS, Azure, or GCP

Preferred / Good to Have :

- Experience with LLMs, RAG pipelines, vector databases (FAISS, Pinecone, Weaviate, Elasticsearch)

- Experience with streaming or real-time ML systems

- Knowledge of data engineering concepts and tools (Spark, Kafka, Databricks)

- Experience with model explainability tools (SHAP, LIME)

- Familiarity with security, privacy, and responsible AI practices

- Certifications in AI/ML or Cloud platforms (AWS/GCP/Azure)